!9701 add CPU OnesLike and ZerosLike

From: @zhao_ting_v
Reviewed-by: @liangchenghui,@wuxuejian
Signed-off-by: @wuxuejian

mindspore/ccsrc/backend/kernel_compiler/cpu/arithmetic_self_cpu_kernel.cc

@@ -35,6 +35,20 @@ void Neg(const T *in, T *out, size_t start, size_t end) {
     out[i] = -in[i];
   }
 }
+
+template <typename T>
+void OnesLike(const T *in, T *out, size_t start, size_t end) {
+  for (size_t i = start; i < end; i++) {
+    out[i] = static_cast<T>(1);
+  }
+}
+
+template <typename T>
+void ZerosLike(const T *in, T *out, size_t start, size_t end) {
+  for (size_t i = start; i < end; i++) {
+    out[i] = static_cast<T>(0);
+  }
+}
 }  // namespace
 
 void ArithmeticSelfCPUKernel::InitKernel(const CNodePtr &kernel_node) {
@@ -42,6 +56,10 @@ void ArithmeticSelfCPUKernel::InitKernel(const CNodePtr &kernel_node) {
   std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node);
   if (kernel_name == prim::kPrimSquare->name()) {
     operate_type_ = SQUARE;
+  } else if (kernel_name == prim::kPrimOnesLike->name()) {
+    operate_type_ = ONESLIKE;
+  } else if (kernel_name == prim::kPrimZerosLike->name()) {
+    operate_type_ = ZEROSLIKE;
   } else if (kernel_name == prim::kPrimNeg->name()) {
     operate_type_ = NEG;
   }
@@ -89,6 +107,10 @@ void ArithmeticSelfCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs
       threads.emplace_back(std::thread(Square<T>, input, output, start, end));
     } else if (operate_type_ == NEG) {
       threads.emplace_back(std::thread(Neg<T>, input, output, start, end));
+    } else if (operate_type_ == ONESLIKE) {
+      threads.emplace_back(std::thread(OnesLike<T>, input, output, start, end));
+    } else if (operate_type_ == ZEROSLIKE) {
+      threads.emplace_back(std::thread(ZerosLike<T>, input, output, start, end));
     }
     start += once_compute_size;
   }

mindspore/ccsrc/backend/kernel_compiler/cpu/arithmetic_self_cpu_kernel.h

@@ -46,6 +46,14 @@ MS_REG_CPU_KERNEL(Neg, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAt
                   ArithmeticSelfCPUKernel);
 MS_REG_CPU_KERNEL(Neg, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
                   ArithmeticSelfCPUKernel);
+MS_REG_CPU_KERNEL(ZerosLike, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+                  ArithmeticSelfCPUKernel);
+MS_REG_CPU_KERNEL(ZerosLike, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+                  ArithmeticSelfCPUKernel);
+MS_REG_CPU_KERNEL(OnesLike, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+                  ArithmeticSelfCPUKernel);
+MS_REG_CPU_KERNEL(OnesLike, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+                  ArithmeticSelfCPUKernel);
 }  // namespace kernel
 }  // namespace mindspore
 

mindspore/ccsrc/backend/kernel_compiler/cpu/cpu_kernel.h

@@ -69,7 +69,9 @@ enum OperateType {
   ABSGRAD,
   TANHGRAD,
   SQRTGRAD,
-  SIGMOIDGRAD
+  SIGMOIDGRAD,
+  ONESLIKE,
+  ZEROSLIKE
 };
 
 class CPUKernel : public kernel::KernelMod {

mindspore/core/base/core_ops.h

@@ -183,6 +183,7 @@ inline const PrimitivePtr kPrimRelu = std::make_shared<Primitive>("ReLU");
 inline const PrimitivePtr kPrimRelu6 = std::make_shared<Primitive>("ReLU6");
 inline const PrimitivePtr kPrimReluV2 = std::make_shared<Primitive>("ReLUV2");
 inline const PrimitivePtr kPrimZerosLike = std::make_shared<Primitive>("ZerosLike");
+inline const PrimitivePtr kPrimOnesLike = std::make_shared<Primitive>("OnesLike");
 inline const PrimitivePtr kPrimBpropCut = std::make_shared<Primitive>("bprop_cut");
 inline const PrimitivePtr kPrimFakeQuantPerLayer = std::make_shared<Primitive>("FakeQuantPerLayer");
 inline const PrimitivePtr kPrimFakeQuantPerChannel = std::make_shared<Primitive>("FakeQuantPerChannel");

mindspore/ops/operations/array_ops.py

@@ -1262,7 +1262,7 @@ class OnesLike(PrimitiveWithInfer):
         Tensor, has the same shape and type as `input_x` but filled with ones.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> oneslike = ops.OnesLike()

tests/st/ops/cpu/test_oneslike_op.py

@@ -0,0 +1,58 @@
+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import pytest
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+
+class NetOnesLike(nn.Cell):
+    def __init__(self):
+        super(NetOnesLike, self).__init__()
+        self.ones_like = P.OnesLike()
+
+    def construct(self, x):
+        return self.ones_like(x)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu_training
+@pytest.mark.env_onecard
+def test_OnesLike():
+    x0_np = np.random.uniform(-2, 2, (2, 3, 4, 4)).astype(np.float32)
+    x1_np = np.random.uniform(-2, 2, 1).astype(np.float32)
+
+    x0 = Tensor(x0_np)
+    x1 = Tensor(x1_np)
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    ones_like = NetOnesLike()
+    output0 = ones_like(x0)
+    expect0 = np.ones_like(x0_np)
+    diff0 = output0.asnumpy() - expect0
+    error0 = np.ones(shape=expect0.shape) * 1.0e-5
+    assert np.all(diff0 < error0)
+    assert output0.shape == expect0.shape
+
+    output1 = ones_like(x1)
+    expect1 = np.ones_like(x1_np)
+    diff1 = output1.asnumpy() - expect1
+    error1 = np.ones(shape=expect1.shape) * 1.0e-5
+    assert np.all(diff1 < error1)
+    assert output1.shape == expect1.shape

tests/st/ops/cpu/test_zeroslike_op.py

@@ -0,0 +1,58 @@
+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import pytest
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+
+class NetZerosLike(nn.Cell):
+    def __init__(self):
+        super(NetZerosLike, self).__init__()
+        self.zeros_like = P.ZerosLike()
+
+    def construct(self, x):
+        return self.zeros_like(x)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu_training
+@pytest.mark.env_onecard
+def test_ZerosLike():
+    x0_np = np.random.uniform(-2, 2, (2, 3, 4, 4)).astype(np.float32)
+    x1_np = np.random.uniform(-2, 2, 1).astype(np.float32)
+
+    x0 = Tensor(x0_np)
+    x1 = Tensor(x1_np)
+
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    zeros_like = NetZerosLike()
+    output0 = zeros_like(x0)
+    expect0 = np.zeros_like(x0_np)
+    diff0 = output0.asnumpy() - expect0
+    error0 = np.ones(shape=expect0.shape) * 1.0e-5
+    assert np.all(diff0 < error0)
+    assert output0.shape == expect0.shape
+
+    output1 = zeros_like(x1)
+    expect1 = np.zeros_like(x1_np)
+    diff1 = output1.asnumpy() - expect1
+    error1 = np.ones(shape=expect1.shape) * 1.0e-5
+    assert np.all(diff1 < error1)
+    assert output1.shape == expect1.shape